A framework of rapid regional tsunami damage recognition from post-event TerraSAR-X imagery using deep neural networks

Near real-time building damage mapping is an indispensable prerequisite for governments to make decisions for disaster relief. With high-resolution synthetic aperture radar (SAR) systems, such as TerraSAR-X, the provision of such products in a fast and effective way becomes possible. In this letter, a deep learning-based framework for rapid regional tsunami damage recognition using post-event SAR imagery is proposed. To perform such a rapid damage mapping, a series of tile-based image split analysis is employed to generate the data set. Next, a selection algorithm with the SqueezeNet network is developed to swiftly distinguish between built-up (BU) and nonbuilt-up regions. Finally, a recognition algorithm with a modified wide residual network is developed to classify the BU regions into wash away, collapsed, and slightly damaged regions. Experiments performed on the TerraSAR-X data from the 2011 Tohoku earthquake and tsunami in Japan show a BU region extraction accuracy of 80.4% and a damage-level recognition accuracy of 74.8%, respectively. Our framework takes around 2 h to train on a new region, and only several minutes for prediction.This work was supported in part by JST CREST, Japan, under Grant JPMJCR1411 and in part by the China Scholarship Council. (JPMJCR1411 - JST CREST, Japan; China Scholarship Council

river morphology monitoring using multitemporal sar data preliminary results

AbstractIn this paper, we test the capability of satellite synthetic aperture radar (SAR) images to enhance the monitoring of river geomorphological processes. The proposed approach exploits the recently introduced Level-α products. These products are bi-temporal RGB composites in which the association color-object, being physical-based, is stable whatever the scene is considered. This favors the detection of temporary rivers' characteristics for classification purposes in a change-detection environment. The case study was implemented on the Orco river (northwest Italy), where a set of 39 COSMO-SkyMed SAR stripmap images acquired from October 2008 to November 2014 was used to monitor channel planform changes. This preliminary study is devoted to assess the suitability of Level-α images for geomorphologist, with particular reference to the detection of phenomena of interest in river monitoring. This is prior for semi-automatic or automatic classification activities

Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics

Mapping landscape patterns and dynamics is essential to various scientific domains and many practical applications. The availability of large-scale and high-resolution light detection and ranging (LiDAR) remote sensing data provides tremendous opportunities to unveil complex landscape patterns and better understand landscape dynamics from a 3D perspective. LiDAR data have been applied to diverse remote sensing applications where large-scale landscape mapping is among the most important topics. While researchers have used LiDAR for understanding landscape patterns and dynamics in many fields, to fully reap the benefits and potential of LiDAR is increasingly dependent on advanced cyberGIS and deep learning approaches. In this context, the central goal of this dissertation is to develop a suite of innovative cyberGIS-enabled deep-learning frameworks for combining LiDAR and optical remote sensing data to analyze landscape patterns and dynamics with four interrelated studies. The first study demonstrates a high-accuracy land-cover mapping method by integrating 3D information from LiDAR with multi-temporal remote sensing data using a 3D deep-learning model. The second study combines a point-based classification algorithm and an object-oriented change detection strategy for urban building change detection using deep learning. The third study develops a deep learning model for accurate hydrological streamline detection using LiDAR, which has paved a new way of harnessing LiDAR data to map landscape patterns and dynamics at unprecedented computational and spatiotemporal scales. The fourth study resolves computational challenges in handling remote sensing big data and deep learning of landscape feature extraction and classification through a cutting-edge cyberGIS approach

How Could Unmanned Aerial Systems (UAS) Be Used for Ecohydrological and Ecosystem Research? Experiences of First Operations with UAS in River Flood Plains of Northern Mongolia

This paper proposes the use of unmanned aerial systems (UAS) as a method for monitoring biotic resources and ecohydrological systems in river floodplains. Small scale mapping based on LANDSAT and SRTM or ASTER data is of limited applicability since a spatial resolution of 30 to 90 m is not sufficient to meet the demands of habitat mapping and large scale 3D -modelling. Newer satellites like WorldView2 and SENTINEL (space mission from European Space Agency within the Copernicus Programme) could be an option to gain a 0.5 m resolution, but the availability of image data is limited. UAS allow the collection of very high spatial and temporal resolution image data and the generation of digital elevation models (DEM). A spatial resolution of less than 10 cm and multispectral or hyperspectral image data, which can be provided by UAS sensors, is needed for mapping of habitats and riparian vegetation. Indicators for water quality such as chlorophyll (a) and suspended matter concentration can be efficiently derived from multispectral image data. Thermal image data, which can also be recorded by UAS-borne sensors, provides information on thermal heterogeneity of water temperature and the interaction of river and groundwater discharge from the river floodplain. In addition, cloud cover rarely affects UAS-generated aerial images because flying altitudes are usually low and flight missions can be timed very flexibly. UAS are also much more cost-effective to operate than manned aircraft. In a first field survey in September 2012, several field plots were investigated in northern Mongolia in different watersheds of the Selenge River Basin (SRB) with varying types of land use and environmental impacts. The regional focus was on the Kharaa River Basin (KRB), which is a paradigm for transformation from nearly natural conditions to an increasingly altered state by economic activities. Within the BMBF funded project “Integrated Water Resources Management in Central Asia: model region Mongolia (MoMo)” the actual situation of water quality, quantity and ecological impacts in this area has been investigated since 2006. A first analysis of nutrient and ecological gradients of the Kharaa Rver Basin indicates a ‘good’ chemical and ecological status for the headwaters and some parts of the middle reaches. Evidence for initial processes of ecosystem degradation and biodiversity loss were detected in the middle and increasingly in the lower reaches. Despite many efforts, several questions remained unsolved. Among them, the impact of erosion and particle transport on ecosystem degradation is a key issue. Fine sediment intrusion caused by erosion predominantly from the river banks but also from upland areas seems to be the most likely cause. However, based on the experiences of our existing monitoring scheme with a combination of intense fieldwork and continuous measuring with data loggers, the need of more spatial information (e.g. riparian vegetation structure, hydromorphology) with a high resolution became evident to confirm this hypothesis. Therefore, an unmanned aerial vehicle (UAV) equipped with a calibrated RGB camera was used to record image data for photogrammetric processing. DEM and orthophotos as well as spherical panoramic views were derived. Furthermore, thermal image data were terrestrially collected using an Infratec Variocam hr. Integration of thermal, multi- or hyperspectral sensors on various UAS (e.g. Archaeocopter), as well as analysis algorithms are the next steps for future work. The applicability of remote sensing approaches is discussed to better foster the development of ground truthing for a sustainable river basin management plan. The application of UAS offers a sound scientific base to assess especially the riparian zones in areas with difficult access

A landsat remote sensing study of vegetation growing on mineralized terrain

Imperial Users onl

Remote sensing of geomorphodiversity linked to biodiversity — part III: traits, processes and remote sensing characteristics

Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed